In this notebook, we are using the tmb_genomic.tsv file
generated from the 01-preprocess-data.Rmd script.
suppressPackageStartupMessages({
library(tidyverse)
})
# Detect the ".git" folder. This will be in the project root directory.
# Use this as the root directory to ensure proper sourcing of functions
# no matter where this is called from.
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
scratch_dir <- file.path(root_dir, "scratch")
analysis_dir <- file.path(root_dir, "analyses", "tmb-vaf-longitudinal")
input_dir <- file.path(analysis_dir, "input")
# Input files
tmb_genomic_file <- file.path(scratch_dir, "tmb_genomic.tsv")
tumor_descriptor_color_palette_file <- file.path(root_dir, "figures", "palettes", "tumor_descriptor_color_palette.tsv")
# File path to plots directory
plots_dir <-
file.path(analysis_dir, "plots")
if (!dir.exists(plots_dir)) {
dir.create(plots_dir)
}
source(paste0(analysis_dir, "/util/function-create-barplot.R"))
source(paste0(root_dir, "/figures/scripts/theme.R"))
# Read and process tmb_genomic file
tmb_genomic_all <- readr::read_tsv(tmb_genomic_file, guess_max = 100000, show_col_types = FALSE)
# Are there any samples with both WGS and WXS?
tmb_genomic_all %>%
unique() %>%
arrange(Kids_First_Participant_ID, experimental_strategy) %>%
group_by(Kids_First_Participant_ID) %>%
dplyr::summarise(experimental_strategy_sum = str_c(experimental_strategy, collapse = ";"))
# Yes, they are, so let's remove these from downstream analyses.
tmb_genomic <- tmb_genomic_all %>%
filter(!experimental_strategy == "WXS") %>%
mutate(tumor_descriptor_order = case_when(grepl("Diagnosis", tumor_descriptor) ~ "1",
grepl("Recurrence", tumor_descriptor) ~ "2",
grepl("Progressive", tumor_descriptor) ~ "3",
grepl("Deceased", tumor_descriptor) ~ "4",
grepl("Second Malignancy", tumor_descriptor) ~ "5",
grepl("Unavailable", tumor_descriptor) ~ "6"),
patient_id = paste(short_histology, Kids_First_Participant_ID, sep = "_"),
log2_tmb = log2(tmb)) %>%
arrange(tumor_descriptor_order, descriptors) %>%
# create plot order
mutate(plot_order = row_number())
# Read color palette
tumor_descriptor_color_palette <- readr::read_tsv(tumor_descriptor_color_palette_file, guess_max = 100000, show_col_types = FALSE)
We will explore TMB per Kids_First_Participant_ID over
time by creating stacked barplots.
# Define parameters for function
ylim <- 360
tmb_df <- tmb_genomic
# Run function
fname <- paste0(plots_dir, "/", "TMB-genomic.pdf")
print(fname)
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/TMB-genomic.pdf"
p <- create_stacked_barplot(tmb_df = tmb_df, ylim = ylim)
pdf(file = fname, width = 15, height = 6)
print(p)
dev.off()
quartz_off_screen
2
Attention: Hypermutant TMB defined as ≥10 Mb, and Ultrahypermutant TMB defined as ≥100 mutations/Mb (https://pubmed.ncbi.nlm.nih.gov/29056344/).
Here, we notice that there are samples with high TMB (hyper-mutant samples). Next, we will exclude these samples (threshold >= 10) from downstream analysis. Attention is needed in cases with high number of mutations in only one timepoint as this will lead to un-matched longitudinal samples. We will also remove those so we always have matched longitudinal samples.
# Filter df
tmb_genomic_filter <- tmb_genomic %>%
filter(!tmb >= 10) %>%
unique() %>%
arrange(Kids_First_Participant_ID, tumor_descriptor) %>%
group_by(Kids_First_Participant_ID) %>%
dplyr::summarise(tumor_descriptor_sum = str_c(tumor_descriptor, collapse = ";")) %>%
filter(!tumor_descriptor_sum %in% c("Diagnosis", "Progressive", "Recurrence")) %>%
dplyr::left_join(tmb_genomic, by = c("Kids_First_Participant_ID", "tumor_descriptor_sum")) %>%
mutate(cancer_group_sum = ifelse(short_histology == "HGAT", "High-grade glioma",
ifelse(short_histology == "LGAT", "Low-grade glioma", "Other cancer group")),
cancer_group_sum = replace_na(cancer_group_sum, "Other")) %>%
drop_na(tmb) %>%
arrange(tumor_descriptor_order, descriptors) %>%
# create plot order
mutate(plot_order = row_number())
# Define parameters for function
ylim <- 12.5
tmb_df <- tmb_genomic_filter
# Run function
fname <- paste0(plots_dir, "/", "TMB-genomic-no-hypermutants.pdf")
print(fname)
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/TMB-genomic-no-hypermutants.pdf"
p <- create_stacked_barplot(tmb_df = tmb_df, ylim = ylim)
pdf(file = fname, width = 25, height = 8)
print(p)
dev.off()
quartz_off_screen
2
We will explore TMB per cancer group over time by creating dumbbell plots. We classified by using cancer types with the highest number of samples (High- and Low-grade gliomas) versus any other cancer groups.
df_ct <- tmb_genomic_filter %>%
arrange(patient_id, tumor_descriptor_order, descriptors) %>%
# create plot order
mutate(plot_order = row_number())
cancer_groups <- unique(as.character(df_ct$cancer_group_sum))
cancer_groups <- sort(cancer_groups, decreasing = FALSE)
print(cancer_groups)
for (i in seq_along(cancer_groups)) {
print(i)
df_ct_sub <- df_ct %>%
filter(cancer_group_sum == cancer_groups [i])
if (i == 1) {
print(cancer_groups [i])
# Define parameters for function
ylim <- 8
} else if (i == 2) {
print(cancer_groups [i])
# Define parameters for function
ylim <- 4
} else {
print(cancer_groups [i])
# Define parameters for function
ylim <- 4
}
# Name plots
fname <- paste0(plots_dir, "/", "TMB-genomic-dumbbell", "-", cancer_groups[i], ".pdf")
print(fname)
# Run function
p <- create_dumbbell_ct(tmb_df = df_ct_sub,
ylim = ylim,
ct_id = cancer_groups[i])
pdf(file = fname, width = 18, height = 10)
print(p)
dev.off()
}
for (i in seq_along(cancer_groups)) {
print(i)
df_ct_sub <- df_ct %>%
filter(cancer_group_sum == cancer_groups [i])
if (i == 1) {
print(cancer_groups [i])
# Define parameters for function
ylim <- 260
} else if (i == 2) {
print(cancer_groups [i])
# Define parameters for function
ylim <- 150
} else {
print(cancer_groups [i])
# Define parameters for function
ylim <- 150
}
# Name plots
fname <- paste0(plots_dir, "/", "Mutations-genomic-dumbbell", "-", cancer_groups[i], ".pdf")
print(fname)
# Run function
p <- create_dumbbell_ct_mut(tmb_df = df_ct_sub,
ylim = ylim,
ct_id = cancer_groups[i])
pdf(file = fname, width = 18, height = 10)
print(p)
dev.off()
}
Here, we want to explore the number of mutations per timepoint and biospecimen sample per patient case.
tmb_genomic_filter_samples <- tmb_genomic_filter %>%
arrange(tumor_descriptor_order, Kids_First_Biospecimen_ID) %>%
# create plot order
mutate(plot_order = row_number())
samples <- unique(as.character(tmb_genomic_filter_samples$Kids_First_Participant_ID))
print(samples)
[1] "PT_TKWTTRQ7" "PT_XHYBZKCX" "PT_3T3VGWC6" "PT_K8ZV7APT" "PT_962TCBVR" "PT_XA98HG1C" "PT_02J5CWN5" "PT_YGN06RPZ" "PT_S4YNE17X" "PT_1ZAWNGWT"
[11] "PT_QH9H491G" "PT_RJ1TJ2KH" "PT_Z4GS3ZQQ" "PT_3R0P995B" "PT_99S5BPE3" "PT_JNEV57VK" "PT_82MX6J77" "PT_2FVTD0WR" "PT_W6AWJJK7" "PT_ZZRBX5JT"
[21] "PT_3VCS1PPF" "PT_FN4GEEFR" "PT_MNSEJCDM" "PT_T2M1338J" "PT_KMHGNCNR" "PT_AQWDQW27" "PT_37B5JRP1" "PT_89XRZBSG" "PT_ZMKMKCFQ" "PT_JSFBMK5V"
[31] "PT_CXT81GRM" "PT_98QMQZY7" "PT_00G007DM" "PT_XTVQB9S4" "PT_PR4YBBH3" "PT_7M2PGCBV" "PT_ESHACWF6" "PT_N8W26H19" "PT_25Z2NX27" "PT_NPETR8RY"
[41] "PT_JP1FDKN9" "PT_9S6WMQ92" "PT_PFA762TK" "PT_HJMP6PH2" "PT_WP871F5S" "PT_MDWPRDBT" "PT_HFQNKP5X" "PT_2ECVKTTQ" "PT_8GN3TQRM" "PT_9PJR0ZK7"
[51] "PT_S2SQJVGK" "PT_2YT37G8P" "PT_DFQAH7RS" "PT_1H2REHT2"
for (i in seq_along(samples)) {
print(i)
tmb_sub <- tmb_genomic_filter_samples %>%
filter(Kids_First_Participant_ID == samples[i])
if (i %in% c(42, 37, 16, 1, 38, 52)) { # "PT_9S6WMQ92", "PT_ESHACWF6", "PT_JNEV57VK", "PT_TKWTTRQ7", "PT_N8W26H19", "PT_2YT37G8P"
print(samples[i])
# Define parameters for function
ylim <- 260
} else if (i %in% c(27, 31, 30, 6, 38, 28, 15, 49, 47, 46, 50, 54)) {
print(samples[i])
# Define parameters for function
ylim <- 100 # "PT_37B5JRP1", "PT_CXT81GRM", "PT_JSFBMK5V", "PT_XA98HG1C", "PT_N8W26H19", "PT_89XRZBSG","PT_99S5BPE3", "PT_8GN3TQRM", "PT_HFQNKP5X", "PT_MDWPRDBT", "PT_9PJR0ZK7", "PT_1H2REHT2"
} else {
print(samples[i])
# Define parameters for function
ylim <- 50
}
# Run function
fname <- paste0(plots_dir, "/", samples[i], "-TMB-barplot.pdf")
print(fname)
p <- create_barplot_sample(tmb_df = tmb_sub,
ylim = ylim,
sid = samples[i])
pdf(file = fname, width = 5, height = 4)
print(p)
dev.off()
}
[1] 1
[1] "PT_TKWTTRQ7"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_TKWTTRQ7-TMB-barplot.pdf"
[1] 2
[1] "PT_XHYBZKCX"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_XHYBZKCX-TMB-barplot.pdf"
[1] 3
[1] "PT_3T3VGWC6"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_3T3VGWC6-TMB-barplot.pdf"
[1] 4
[1] "PT_K8ZV7APT"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_K8ZV7APT-TMB-barplot.pdf"
[1] 5
[1] "PT_962TCBVR"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_962TCBVR-TMB-barplot.pdf"
[1] 6
[1] "PT_XA98HG1C"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_XA98HG1C-TMB-barplot.pdf"
[1] 7
[1] "PT_02J5CWN5"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_02J5CWN5-TMB-barplot.pdf"
[1] 8
[1] "PT_YGN06RPZ"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_YGN06RPZ-TMB-barplot.pdf"
[1] 9
[1] "PT_S4YNE17X"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_S4YNE17X-TMB-barplot.pdf"
[1] 10
[1] "PT_1ZAWNGWT"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_1ZAWNGWT-TMB-barplot.pdf"
[1] 11
[1] "PT_QH9H491G"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_QH9H491G-TMB-barplot.pdf"
[1] 12
[1] "PT_RJ1TJ2KH"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_RJ1TJ2KH-TMB-barplot.pdf"
[1] 13
[1] "PT_Z4GS3ZQQ"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_Z4GS3ZQQ-TMB-barplot.pdf"
[1] 14
[1] "PT_3R0P995B"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_3R0P995B-TMB-barplot.pdf"
[1] 15
[1] "PT_99S5BPE3"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_99S5BPE3-TMB-barplot.pdf"
[1] 16
[1] "PT_JNEV57VK"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_JNEV57VK-TMB-barplot.pdf"
[1] 17
[1] "PT_82MX6J77"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_82MX6J77-TMB-barplot.pdf"
[1] 18
[1] "PT_2FVTD0WR"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_2FVTD0WR-TMB-barplot.pdf"
[1] 19
[1] "PT_W6AWJJK7"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_W6AWJJK7-TMB-barplot.pdf"
[1] 20
[1] "PT_ZZRBX5JT"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_ZZRBX5JT-TMB-barplot.pdf"
[1] 21
[1] "PT_3VCS1PPF"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_3VCS1PPF-TMB-barplot.pdf"
[1] 22
[1] "PT_FN4GEEFR"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_FN4GEEFR-TMB-barplot.pdf"
[1] 23
[1] "PT_MNSEJCDM"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_MNSEJCDM-TMB-barplot.pdf"
[1] 24
[1] "PT_T2M1338J"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_T2M1338J-TMB-barplot.pdf"
[1] 25
[1] "PT_KMHGNCNR"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_KMHGNCNR-TMB-barplot.pdf"
[1] 26
[1] "PT_AQWDQW27"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_AQWDQW27-TMB-barplot.pdf"
[1] 27
[1] "PT_37B5JRP1"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_37B5JRP1-TMB-barplot.pdf"
[1] 28
[1] "PT_89XRZBSG"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_89XRZBSG-TMB-barplot.pdf"
[1] 29
[1] "PT_ZMKMKCFQ"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_ZMKMKCFQ-TMB-barplot.pdf"
[1] 30
[1] "PT_JSFBMK5V"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_JSFBMK5V-TMB-barplot.pdf"
[1] 31
[1] "PT_CXT81GRM"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_CXT81GRM-TMB-barplot.pdf"
[1] 32
[1] "PT_98QMQZY7"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_98QMQZY7-TMB-barplot.pdf"
[1] 33
[1] "PT_00G007DM"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_00G007DM-TMB-barplot.pdf"
[1] 34
[1] "PT_XTVQB9S4"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_XTVQB9S4-TMB-barplot.pdf"
[1] 35
[1] "PT_PR4YBBH3"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_PR4YBBH3-TMB-barplot.pdf"
[1] 36
[1] "PT_7M2PGCBV"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_7M2PGCBV-TMB-barplot.pdf"
[1] 37
[1] "PT_ESHACWF6"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_ESHACWF6-TMB-barplot.pdf"
[1] 38
[1] "PT_N8W26H19"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_N8W26H19-TMB-barplot.pdf"
[1] 39
[1] "PT_25Z2NX27"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_25Z2NX27-TMB-barplot.pdf"
[1] 40
[1] "PT_NPETR8RY"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_NPETR8RY-TMB-barplot.pdf"
[1] 41
[1] "PT_JP1FDKN9"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_JP1FDKN9-TMB-barplot.pdf"
[1] 42
[1] "PT_9S6WMQ92"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_9S6WMQ92-TMB-barplot.pdf"
[1] 43
[1] "PT_PFA762TK"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_PFA762TK-TMB-barplot.pdf"
[1] 44
[1] "PT_HJMP6PH2"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_HJMP6PH2-TMB-barplot.pdf"
[1] 45
[1] "PT_WP871F5S"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_WP871F5S-TMB-barplot.pdf"
[1] 46
[1] "PT_MDWPRDBT"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_MDWPRDBT-TMB-barplot.pdf"
[1] 47
[1] "PT_HFQNKP5X"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_HFQNKP5X-TMB-barplot.pdf"
[1] 48
[1] "PT_2ECVKTTQ"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_2ECVKTTQ-TMB-barplot.pdf"
[1] 49
[1] "PT_8GN3TQRM"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_8GN3TQRM-TMB-barplot.pdf"
[1] 50
[1] "PT_9PJR0ZK7"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_9PJR0ZK7-TMB-barplot.pdf"
[1] 51
[1] "PT_S2SQJVGK"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_S2SQJVGK-TMB-barplot.pdf"
[1] 52
[1] "PT_2YT37G8P"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_2YT37G8P-TMB-barplot.pdf"
[1] 53
[1] "PT_DFQAH7RS"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_DFQAH7RS-TMB-barplot.pdf"
[1] 54
[1] "PT_1H2REHT2"
[1] "/Users/chronia/CHOP/GitHub/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/PT_1H2REHT2-TMB-barplot.pdf"
sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.6.0 gridExtra_2.3 cowplot_1.1.1 ggthemes_4.2.4 Rmisc_1.5.1 plyr_1.8.8 lattice_0.21-8 lubridate_1.9.2
[9] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[17] tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.11 rprojroot_2.0.3 digest_0.6.33 utf8_1.2.3 R6_2.5.1 backports_1.4.1 evaluate_0.21
[8] pillar_1.9.0 rlang_1.1.1 rstudioapi_0.15.0 data.table_1.14.8 car_3.1-2 jquerylib_0.1.4 rmarkdown_2.23
[15] labeling_0.4.2 bit_4.0.5 munsell_0.5.0 broom_1.0.5 compiler_4.2.3 xfun_0.39 pkgconfig_2.0.3
[22] htmltools_0.5.5 tidyselect_1.2.0 fansi_1.0.4 crayon_1.5.2 tzdb_0.4.0 withr_2.5.0 jsonlite_1.8.7
[29] gtable_0.3.3 lifecycle_1.0.3 magrittr_2.0.3 scales_1.2.1 cli_3.6.1 stringi_1.7.12 vroom_1.6.3
[36] cachem_1.0.8 carData_3.0-5 farver_2.1.1 ggsignif_0.6.4 bslib_0.5.0 generics_0.1.3 vctrs_0.6.3
[43] RColorBrewer_1.1-3 tools_4.2.3 bit64_4.0.5 glue_1.6.2 hms_1.1.3 abind_1.4-5 parallel_4.2.3
[50] fastmap_1.1.1 yaml_2.3.7 timechange_0.2.0 colorspace_2.1-0 rstatix_0.7.2 knitr_1.43 sass_0.4.7